Explainable AI for resource-aware intervention prioritization of digital burnout in healthcare workplaces.

PURPOSE: Digital burnout is an emerging workforce risk in digitally intensive healthcare systems, yet existing studies remain largely descriptive and provide limited guidance for prioritizing modifiable risk factors. This study develops an explainable artificial intelligence framework for model-based prioritization of digital burnout drivers among healthcare professionals. METHODS: Cross-sectional survey data were collected from Malaysian healthcare professionals (N = 400). Digital burnout was modelled as a continuous outcome using digital job demands, organizational and personal resources, technostress, and digital minimalism practices as predictors. Linear regression, random forest, and gradient boosting models were evaluated using train-test validation, five-fold cross-validation, and repeated random train-test resampling. SHapley Additive exPlanations were used to characterize predictor contributions and heterogeneity in model predictions. Constrained counterfactual scenario analysis was then conducted to estimate model-based changes in predicted burnout under alternative adjustment scenarios. RESULTS: Gradient boosting demonstrated stable out-of-sample performance, achieving a mean R2 of 0.653 in five-fold cross-validation and 0.660 across 100 repeated 80/20 train-test splits. SHAP analysis identified digital minimalism practices and technostress as the dominant opposing predictors of digital burnout. Single-factor scenario prioritization varied across individuals, risk thresholds, and perturbation magnitudes, whereas the combined DMP-enhancement and technostress-reduction scenario produced the largest model-predicted burnout reduction across all evaluated conditions. CONCLUSION: The proposed framework advances digital burnout research from prediction toward explainable, resource-aware decision support. The simulated estimates are non-causal and require longitudinal or experimental validation before being interpreted as real-world intervention effects.